ArticleMolecular genetics and genomics : MGG2015
Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.
Article in Molecular genetics and genomics : MGG, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 71 citations in OpenAlex.
- Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.Scientific reports · 2019Article
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
- Protein remote homology detection based on bidirectional long short-term memory.BMC bioinformatics · 2017Article
- Investigation of the inhibition effect and mechanism of myricetin to Suilysin by molecular modeling.Scientific reports · 2017Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- Prediction of phosphothreonine sites in human proteins by fusing different features.Scientific reports · 2016Article
- iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.Scientific reports · 2016Article
- dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation.Scientific reports · 2016Article
- Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.The Journal of membrane biology · 2016Article
- Recombination spot identification Based on gapped k-mers.Scientific reports · 2016Article
- DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.Scientific reports · 2016Article
- repRNA: a web server for generating various feature vectors of RNA sequences.Molecular genetics and genomics : MGG · 2016Article
- iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions.Scientific reports · 2016Article
- Prefiltering Model for Homology Detection Algorithms on GPU.Evolutionary bioinformatics online · 2016Article
- Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.PloS one · 2016Article
- Protein Remote Homology Detection Based on an Ensemble Learning Approach.BioMed research international · 2016Article
- Predicting cancerlectins by the optimal g-gap dipeptides.Scientific reports · 2015Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein remote homology detection is one of the important tasks in computational proteomics, which is important for basic research and practical application. Currently, the SVM-based discriminative methods have shown superior performance. However, the existing feature vectors still cannot suitably represent the protein sequences, and often lack an interpretable model for analysis of characteristic features. Previous studies showed that sequence-order effects and physicochemical properties are important for representing protein sequences. However, how to use these kinds of information for constructing predictors is still a challenging problem. In this study, in order to incorporate the sequence-order information and physicochemical properties into the prediction, a method called disPseAAC is proposed, in which the feature vector is constructed by combining the occurrences of amino acid pairs within the Chou's pseudo amino acid composition (PseAAC) approach. The predictive performance and computational cost are further improved by employing the principal component analysis strategy. Various experiments are conducted on a benchmark dataset. Experimental results show that disPseAAC achieves an ROC score of 0.922, outperforming some existing state-of-the-art methods. Furthermore, the learnt model can easily be analyzed in terms of discriminative features, and the computational cost of the proposed method is much lower than that of other profile-based methods.
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